Abstract
Background: To improve healthcare services’ quality, countries should measure their health systems’ efficiency and performance byrobust methods.
Objectives: We aimed to develop a national study to measure the efficiency of the health system in Iran.
Methods: The literature review identified several methods for measuring efficiency; the most common one was data envelopment analysis(DEA). We adopted DEA, but its findings were simplistic and inaccurate, so we began to modify the method by determining the weight ofeach indicator. We identified the efficiency measurement indicators, in line with international standards and uniformed units, and thenreadjusted our input/output indicators according to the study context through four expert panels. We collected data and classified theinput/output indicators, followed by determining each indicator’s weight and standard limits. Then we rationalized our previous resultsby applying the revised model. The initial new results of the refined model were valid, accurate, and consistent with previous studies,as approved by experts. We defined proper modeling to achieve the stated objectives. After investigating various DEA models, we finallydesigned a new model that was consistent with the existing data and conditions, entitled EDEA (extended DEA), to analyze other subprojects.
Conclusions: The conventional DEA methods may not be accurate enough to measure health systems’ efficiency. By modifying modelingprocess, we propose a modified DEA with a very low error rate. We suggest that others interested in measuring health system efficiencyadopt our modified approach to increase accuracy and create more meaningful policy-oriented results.
Objectives: We aimed to develop a national study to measure the efficiency of the health system in Iran.
Methods: The literature review identified several methods for measuring efficiency; the most common one was data envelopment analysis(DEA). We adopted DEA, but its findings were simplistic and inaccurate, so we began to modify the method by determining the weight ofeach indicator. We identified the efficiency measurement indicators, in line with international standards and uniformed units, and thenreadjusted our input/output indicators according to the study context through four expert panels. We collected data and classified theinput/output indicators, followed by determining each indicator’s weight and standard limits. Then we rationalized our previous resultsby applying the revised model. The initial new results of the refined model were valid, accurate, and consistent with previous studies,as approved by experts. We defined proper modeling to achieve the stated objectives. After investigating various DEA models, we finallydesigned a new model that was consistent with the existing data and conditions, entitled EDEA (extended DEA), to analyze other subprojects.
Conclusions: The conventional DEA methods may not be accurate enough to measure health systems’ efficiency. By modifying modelingprocess, we propose a modified DEA with a very low error rate. We suggest that others interested in measuring health system efficiencyadopt our modified approach to increase accuracy and create more meaningful policy-oriented results.
| Original language | English |
|---|---|
| Journal | Health Technology Assessment in Action |
| Volume | 6 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 14 Nov 2022 |
Keywords
- Efficiency
- Health System
- Productivity
- Protocol